Hussein Ibrahim Hussein¹, Said Amirul Anwar^2,*, Muhammad Imran Ahmad²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025

Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which… More >

C. Anjali^*, Julia Punitha Malar Dhas, J. Amar Pratap Singh

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1241-1256, 2023, DOI:10.32604/iasc.2023.029678

Abstract The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network… More >

Hameedur Rahman¹, Junaid Tariq^2,*, M. Ali Masood¹, Ahmad F. Subahi³, Osamah Ibrahim Khalaf⁴, Youseef Alotaibi⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5527-5543, 2023, DOI:10.32604/cmc.2023.033190

Abstract Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model… More >

Xi Huang^1,2, Shibin Zhang^1,2,*, Chen Lin^1,2, Jinyue Xia³

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2783-2794, 2023, DOI:10.32604/csse.2023.032190

Abstract In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning. Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster algorithmic processing of data. However,… More >

Yonghui Duan¹, Keqing Zhao^1,*, Yibin Guo², Xiang Wang²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2207-2222, 2023, DOI:10.32604/csse.2023.032297

Abstract A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective weighting method, the commercial housing… More >

Muhammad Junaid Umer¹, Muhammad Sharif¹, Majed Alhaisoni², Usman Tariq³, Ye Jin Kim⁴, Byoungchol Chang^5,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463

Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL)… More >

B. Yasotha^1,*, T. Sasikala², M. Krishnamurthy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1625-1640, 2023, DOI:10.32604/csse.2023.025669

Abstract The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive… More >

Muhammad Irfan¹, Khalil Ullah², Fazal Muhammad^3,*, Salman Khan³, Faisal Althobiani⁴, Muhammad Usman⁵, Mohammed Alshareef⁴, Shadi Alghaffari⁴, Saifur Rahman¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337

Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with… More >

Jithina Jose^*, T. Sasipraba

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 573-588, 2023, DOI:10.32604/iasc.2023.030479

Abstract In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen, water, hydrogen, carbon, nitrogen, sulphur… More >

Farah Mohammad^*, Saad Al-Ahmadi

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552

Abstract

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >